使用AlphaFold 3辅助拓深度学习对快速病毒进化的快速响应.
JunJie Wee1, Guo-Wei Wei1,2,3
1Department of Mathematics, Michigan State University, East Lansing, MI 48824, United States.
Virus evolution
|May 12, 2025
概括
这项研究介绍了一种AlphaFold 3辅助的计算策略,用于预测病毒突变. 该方法准确地预测了结合自由能量的变化,有助于快速应对不断演变的传染性病毒.
科学领域:
- 计算生物学是一种计算生物学.
- 病毒学 病毒学
- 结构生物学是结构生物学.
背景情况:
- 像SARS-CoV-2这样的病毒的快速进化需要有效的计算工具来跟踪,诊断和治疗开发.
- 目前用于变异预测的方法通常依赖于深度突变扫描 (DMS) 和3D蛋白质-蛋白质相互作用 (PPI) 复杂结构,这可能是耗时和昂贵的.
研究的目的:
- 开发一种高效的计算方法来预测病毒突变对蛋白质-蛋白质相互作用和结合的影响.
- 通过拓深度学习 (TDL) 提高对病毒突变发生的深度突变扫描 (DMS) 和结合自由能量 (BFE) 变化的预测.
主要方法:
- 提出了一个AlphaFold 3 (AF3) 辅助的多任务拓拉普拉西安 (MT-TopLap) 策略.
- 结合深度学习与拓数据分析 (TDA) 模型,如持久的拉普拉西安 (PL),以提取PPI的拓和几何特征.
- 使用SARS-CoV-2尖端受体结合域 (RBD) 和人类血管酶转化酶-2 (ACE2) 复合物的实验DMS数据集验证了该策略.
主要成果:
- 与实验结构相比,AF3辅助的MT-TopLap策略表现强,皮尔森相关系数 (PCC) 降低最小,根平均平方误差 (RMSE) 略有增加.
- 在与SARS-CoV-2 HK.3变种DMS数据集进行测试时达到0.81的PCC,表明BFE变化的准确预测.
- 展示了对新实验数据的适应性,证实了其实时应用的潜力.
结论:
- 辅助AF3的MT-TopLap策略提供了一种高效和准确的计算方法来预测病毒突变的影响.
- 这种方法可以通过改善病毒追踪,诊断和治疗设计来加速对新出现的传染性病毒的反应.
- 该战略具有很大的潜力,可以快速有效地适应快速演变的病毒带来的挑战.
关键词:
阿尔法 折叠3 3在SARS-CoV-2变种中.深度突变扫描 (deep mutational scanning) 是一种对突变进行深度扫描的方法.蛋白质蛋白质相互作用拓学深度学习 (deep learning) 是一种学习方式.更多相关视频
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